Description

After exploring the general pattern of modelling GPP vs observational GPP, the next step to identify the specific period when the mismatch between modeled GPP and observed GPP in each site–>focused in the markdown file

step1: tidy the table for GPP simulation vs GPP obs sites

step2: finding the way to separate out the model early simulation period

step1: tidy the table

library(kableExtra)
library("readxl")
table.path<-"C:/Users/yluo/Desktop/CES/Data_for_use/"
my_data <- read_excel(paste0(table.path,"Info_Table_about_Photocold_project.xlsx"), sheet = "Only_sites_earlyGPPest")
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
#   kable_paper(full_width = F, html_font = "Cambria") %>%
#   scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
  kbl(caption = "Summary of sites with early GPP estimation") %>%
  kable_classic(full_width = F, html_font = "Cambria")
Summary of sites with early GPP estimation
SiteName Delay_status Long. Lat. Period PFT Clim. N Calib. Avai.analyzed.years-spring Avai.site-years-spring Avai.analyzed.years-springawinter Avai.site-years-springawinter Reference
DE-Hai Yes 10.45 51.08 2000-2012 DBF Cfb 4247 Y 2000-2012 13 2000-2012 13 Knohl et al. (2003)
US-Syv Yes -89.35 46.24 2001-2014 MF Dfb 2635 Y 2002-2006, 2014 6 2002, 2004-2006,2014 5 Desai et al. (2005)
US-UMB Yes -84.71 45.56 2000-2014 DBF Dfb 4015 Y 2000-2014 15 2000-2014 15 Gough et al. (2013)
US-UMd Yes -84.70 45.56 2007-2014 DBF Dfb 2050 Y 2008-2014 7 2008-2013 6 Gough et al. (2013)
US-WCr Yes -90.08 45.81 1999-2014 DBF Dfb 3425 Y 2000-2006, 2011-2014 11 2000-2006, 2011-2014 11 Cook et al. (2004)
US-Wi3 Yes -91.10 46.63 2002-2004 DBF Dfb 415 NA no years (2002, 2004 lack early doy) 0 no years 0 Noormets et al. (2007)
CA-Man Yes -98.48 55.88 1994-2008 ENF Dfc 1910 NA 2000-2003, 2007-2008 6 2000-2003, 2007 5 Dunn et al. (2007)
CA-NS2 Yes -98.52 55.91 2001-2005 ENF Dfc 1123 NA 2002, 2004 (2003 lack early doy) 2 2002 1 NA
CA-NS4 Yes -98.38 55.91 2002-2005 ENF Dfc 756 NA 2005 (2003 lack early doy) 1 no years 0 NA
CA-NS5 Yes -98.48 55.86 2001-2005 ENF Dfc 1245 NA 2002, 2004-2005 (2003 lack early doy) 3 2002, 2004 2 NA
CA-Qfo Yes -74.34 49.69 2003-2010 ENF Dfc 2416 NA 2004-2010 7 2004-2010 7 Bergeron et al. (2007)
FI-Hyy Yes 24.30 61.85 1996-2014 ENF Dfc 4587 Y 2000-2014 15 2000-2004, 2006-2014 14 Suni et al. (2003)
IT-Tor Yes 7.58 45.84 2008-2014 GRA Dfc 2172 Y 2009-2014 6 2009-2014 6 Galvagno et al. (2013)
Sum NA NA NA NA NA NA 30996 NA NA 92 NA 73 NA

step2: seprate the time period when model early estimation of GPP

Part1: find the method to determine the period that with early GPP estimation

## [1] 13

Part 2: check all the sites

(1) For Dfb:both for MF and DBF sites - Dfb-MF (1 site)

## [1] 6

  • Dfb-DBF (4 sites)
## [1] 15

## [1] 7

## [1] 11

(2) For Dfc:both for GRA and ENF sites

  • Dfc-GRA (1 site)
## [1] 6

  • Dfc-ENF (6 sites)
## [1] 6

## [1] 2

## [1] 1

## [1] 3

## [1] 7

## [1] 15

step3: save the data that label with “is_event”

Summary

steps to determine the “is_event” period

Step1: normlization for all the years in one site

#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)

Step 2:Determine the green-up period for each year(using spline smoothed values):

#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)

  • using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period

    Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)

    also for the data beyond green-up period–> the code of this steps moves to second step

    • at the end, I select the 20 days windows for the rolling mean

    Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period

    • The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).

    • But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:

      1. [peak,265/366]

      2. DoY[1, sos]& DOY[peak,365/366]

      3. [1,sos] & [eos,365/366]

    I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.

    step 5:determine the “is_event” within green-up period

    • After some time of consideration, I took following crition to determine the “is_event”:

      1. during the green-up period (sos,peak)–>the data with GPP biases bigger than 3 SD are classified as the “GPP overestimation points”

      2. For “GPP overestimation points” –> only regard the data points in the first 2/3 green-up period as the “is_event”

      3. For “is_event points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event_less10”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)

      References:

      Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052

      Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108

    step 6:Evaluation “is_event”–>visualization and stats

    • two ways to evaluate if “is_event” is properly determined:

    1. visulization

    2. stats: \[ Pfalse = /frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]